A Python toolkit of observer-based audibility modeling methods
This Python code is derived from the research article "Evaluating and Predicting the Audibility of Acoustic Alarms in the Workplace Using Experimental Methods and Deep Learning" published in the journal Applied Acoustics. It provides a framework for predicting the audibility of acoustic alarms in noise, and includes scripts to reproduce the results
An audibility based approach to predict the head-shadow effect and the speech intelligibility in quiet and noise with a Percutaneous Bone Conduction Device in Single-sided Deaf Subjects
Implements ALCOA-compliant integrity, Ed25519 cryptographic signatures, and mandatory TTL. Features Context-Delta logic to minimize token bloat and latency in multi-agent workflows. A disciplined, vendor-neutral boundary for secure, time-bound, and auditable AI orchestration.
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